Not Applicable.
The present invention relates generally to pest management systems, and more particularly to a method for pest management using pest identification sensors and a network accessible database.
Integrated pest management is an informational science of obtaining accurate information regarding the many factors that affect the density, distribution, and dynamics of pest populations. The ultimate goal has always been to use that information to integrate control measures. Pest control measures are triggered either by the presence of a particular pest or by a threshold density of that pest, taking into consideration the phenology of the affected crop, and the physical and biological characteristics of the environment at a given time. Data on various physical and biological parameters must be collected, tabulated, filtered, statistically analyzed and compared, so that good decisions for effective pest control can be made and implemented in a timely fashion. There is, therefore, an ever-increasing demand for reliable, current data that reflect actual conditions found in the field upon which pest control management decisions can be based.
Pest management, control and monitoring programs frequently suffer from a lack of reliable information. For a monitoring program to be effective, information has to swiftly flow through a sequence that starts with data gathered in the field, which is passed through local supervisors and more central decision makers, and ends up with those who are responsible for the implementation of pest control measures.
A typical management program will include many branches for data management. The branches may have structural differences, may be operated by people belonging to different agencies, and may be located in different areas. Not surprisingly, bottlenecks in the flow of information are common. These bottlenecks may be caused by slow or imprecise data gathering or by slow and inefficient data processing. The result is information flowing too slowly to the decision maker. Further, inefficient or inconsistent data management can result in poorly collected data or a failure to separate relevant information from that which is irrelevant. Problems with information flow may generate reports with little useful information, resulting in poor decision-making and ineffectual control measures. Poor data management is damaging for small programs, but the situation becomes nearly unmanageable when data management problems occur in large area-wide pest management programs.
The food production industry has been plagued not only by pests that compromise crop and food quality but also with the task of managing information to control these pests. Indigenous and established arthropod pests are a major concern for farmers and ranchers and are the subject of study for entire divisions of large governmental agencies. The introduction of exotic pests is especially problematic for the agricultural industry. The industry is affected directly, by pest damage and extra expenses incurred through controlling new exotic pests, and indirectly, through trade barriers aimed at infested commodities by pest-free importing regions. Once a pest is established, the cost of control is permanent. An increase in imported goods, fostered by trade agreements between states, increases the risk of introduction of new pests.
Collaborative efforts will play an ever more critical role in the management of exotic pests. Many regional and even intercontinental task forces have been created to manage and combat exotic insect pests. These task forces require concerted, area-wide interventions, and are usually far more effective than the somewhat erratic, asynchronous interventions that individual farmers may perform when not involved in regionally controlled management efforts.
When an exotic pest is the focus of a management program, it is likely that the program involves different organizations, including agencies from city, county, state, and federal governments, as well as interested private groups. The organization for the monitoring and detection tasks may be flexible and relaxed. Different groups will collect different types of information, based upon their own particular agendas, which is then stored in databases at various locations. It is likely that these databases do not use the same software and are maintained and edited by persons of varying expertise, who use different criteria and protocols to handle and analyze the data. The unexpected detection of an exotic pest results in an emergency situation requiring a drastic change in this flexible organization. Pest eradication requires a program that is well coordinated. For emergency situations the organizational structure of a program has to be well established. All historical data and newly collected data have to be readily available and rapidly analyzed so the emergency regional pest control effort can make rapid, effective decisions.
Existing pest management programs vary in degree of sophistication. Most common is the approach in which farmers spray fields following a calendar schedule. The implementation of control measures is triggered based on historical data and executed regardless of the presence of or the density of the pest. This approach is generally attractive to growers due to its simplicity and ease of implementation. However, this approach frequently results in unnecessary insecticide applications, which may ultimately result in a plethora of agro-ecological problems including environmental contamination, ecological imbalance, and suppression of natural enemy populations.
More sophisticated regional strategies exist that monitor physical and biological environment and use the data to determine if populations are above or below thresholds to determine if control action is needed, referred to in the industry as the xe2x80x9cthresholdxe2x80x9d approach. Such a pest control strategy has the advantage of being a good predictive power of pest population dynamics using modeling techniques. If pest control action is necessary, it is directed to the areas where pest populations are found at higher densities, or where they are escaping their natural enemies"" control. This strategy in turn has a lower impact on the argo-ecosystem, and is the basis for the development of more sustainable agriculture. The difficulty with this approach is that it requires better than average organizational skills, a commitment from the farmer, the use of standardized methods of data collection, and enough allocation of time to perform the careful, consistent monitoring needed to support good decision-making.
Accordingly, there is a need in the art for an improved method of pest management in comparison to the prior art.
In accordance with an embodiment of the present invention, there is provided a method of pest management of crops by a grower. The method includes gathering pest sampling data in connection with a crop of the grower. The pest sampling data includes pest identification information gathered using a pest identification sensor. The pest sampling data further includes locational information thereof. The method further includes transmitting the gathered pest sampling data to a pest sampling database. The pest sampling database includes pest sampling data regarding respective crops from a plurality of other growers. The pest sampling database is in electrical communication with pest management analysis software for generation of pest management analysis. The method further includes electronically receiving the generated pest management analysis.
According to various embodiments, the pest identification sensor may be an acoustic sensor, an optical sensor, or a weight sensor. The method may provide for using at least two different types of pest identification sensors. The pest identification sensor may be deployed in conjunction with a pest trap, and the pest trap may utilize a pest attractant and the pest sampling data includes identification of the attractant. The pest identification sensor may be configured to detect wingbeat information, pest surface characteristics information, and size information. The pest management analysis software may be configured to identify pests based upon the pest identification information and the locational information. The pest sampling data may be gathered utilizing a portable computer, and the pest identification information may be transmitted from the pest identification sensor via a wireless device.
In accordance with another aspect of the present invention, there is provided a method of providing pest management of a plurality of growers. The method includes establishing a relationship with the plurality of growers wherein each of the growers agrees to gather pest sampling data in connection with a crop of the grower. The pest sampling data includes pest identification information gathered using a pest identification sensor. The pest sampling data further includes locational information thereof. The method further includes electronically receiving gathered pest sampling data from the growers. The method further includes electronically storing the pest sampling data in a pest sampling database. The method further includes generating pest management analysis with pest management analysis software using the pest sampling database for a crop of a respective one of the growers.
According to various embodiments, the pest identification sensor may be an acoustic sensor, an optical sensor, or a weight sensor. The method may provide for using at least two different types of pest identification sensors. The pest identification sensor may be deployed in conjunction with a pest trap that utilizes a pest attractant and the pest sampling data includes identification of the attractant. The pest identification sensor may be configured to detect wingbeat information, pest surface characteristics information, or size information. The pest management analysis software may be configured to identify pests based upon the pest identification information and the locational information. The pest management analysis software may be configured to identify pests based upon the pest identification information and pest seasonal activity information, pest circadian rhythm information, pest geographical distribution information, pest habitat information, and pest attractant information.
As such, based on the foregoing, the present invention mitigates the inefficiencies and limitations associated with prior art pest management methods. Accordingly, the present invention represents a significant advance in the art.